For decades, scientists have been observing the mind-boggling vastness of space for signs of extraterrestrial beings. This has led to the creation of agencies such as SETI – Search for Extraterrestrial Intelligence. SETI has developed and funded numerous projects that have used state-of-the-art technology not only to collect information but also to analyze them – all in hopes of finding the elusive signs of life in space. One such project, Breakthrough Listen, has implemented Artificial Intelligence (AI) in its methodology to find patterns in collected data – resulting in the discovery of 72 new Fast Radio Bursts (FRB’s), supposedly originating from an unknown source 3 billion light years away.

Before we delve into how AI aided scientists to discover the new FRB’s, we must first understand the fundamental nature of them. Fast radio waves are bright pulses of radio emission that lasts for mere milliseconds. They are thought to originate from distant galaxies, although the source still remains a mystery. Some theories, however, justify that their properties are consistent with signatures of technology developed by an advanced civilization.

Researches from the University of California, Berkeley, has led Breakthrough Listen since 2012. On 26th August, 2017, they used the Green Bank Telescope in West Virginia for a five-hour period to collect 400 terabytes of data. By analyzing the data using standard computer algorithms, they were able to identify 21 FRB’s during the period. They could only predict that the source, FRB 121102, alternated between periods of quiescence and frenzied activity. However, no further results could be dictated using the classical analysis method.

Next, UC Berkeley Ph.D. student Gerry Zhang and a few collaborators developed a new, powerful machine-learning algorithm – using similar techniques implemented to optimize search engine results. They trained an algorithm known as a convolutional neural network to identify the earlier found FRB’s, before setting it loose on the 2017 dataset. The results were able to identify 72 new FRB’s, which were missed during the earlier analysis. Thus, the discovery brought the total number of detected bursts from FRB 121102 to 300 since 2012.

The new FRB’s discovered by the AI algorithm will help scientists to not only shed light on their nature but also on their source. The results indicate that pulses are not received with a regular pattern if the periodicity is longer than 10 milliseconds. However, shorter duration pulses occur in repetition and might be of interest to physicists to help explain the cause and physics involved in FRB’s. The new FRB’s will also help put constraints on computer models to aid astronomers to discover their enigmatic source.

Gerry Zhang stated, “Whether or not FRBs themselves eventually turn out to be signatures of extraterrestrial technology, Breakthrough Listen is helping to push the frontiers of a new and rapidly growing area of our understanding of the Universe around us.”

Breakthrough Listen is also applying the successful machine learning algorithm to find new kinds of signals that could be emerging from an extraterrestrial civilization. Andrew Siemion, director of the Berkeley SETI Research Center and principal investigator for Breakthrough Listen, has said, “This work is exciting not just because it helps us understand the dynamic behavior of fast radio bursts in more detail, but also because of the promise it shows for using machine learning to detect signals missed by classical algorithms.”